Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/1572

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dc.contributor.author

Singh, J

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dc.contributor.author

Sahoo, Bibhudatta

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dc.date.accessioned

2011-12-22T09:07:14Z

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dc.date.available

2011-12-22T09:07:14Z

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dc.date.issued

2011

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dc.identifier.citation

IJCA Special Issue on 2nd National Conference- Computing, Communication and Sensor Network (CCSN) (4):13-17, 2011. Published by Foundation of Computer Science, New York, USA.

en

dc.identifier.uri

http://hdl.handle.net/2080/1572

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dc.description

Copyright belongs to International Journal of Computer Applications

en

dc.description.abstract

Failures of software are mainly due to the faulty project management practices, which includes effort estimation. Continuous changing scenarios of software development technology makes effort estimation more challenging. Ability of ANN(Artificial Neural Network) to model a complex set of relationship between the dependent variable (effort) and the independent variables (cost drivers) makes it as a potential tool for estimation. This paper presents a performance analysis of different ANNs in effort estimation. We have simulated four types of ANN created by MATLAB10 NNTool using NASA dataset